Sustainable Extensibility in Big Data
报告题目：Sustainable Extensibility in Big Data
报告人：Dean Allemang
时间：2015年10月26日 9：00-10：00
地点：数学馆201报告厅
报告摘要：
In this talk, I will present a big-data system that is in production at a major international bank to manage reference data for a variety of uses, including Anti-money laundering and know your customer(AML/KYC), risk assessment, and regulatory compliance. The system addresses two common big-data challenges for enterprise data systems, especially in large financial institutions. The first is sustainable extensibility - the system can be extended to include new data sources on an ongoing basis, allowing it to extend its coverage as more stakeholders join in (a common occurrence in today's world of corporate conglomerates). The second is bitemporality - every piece of data in the system is indexed based on system time (when the fact was known) and business time (when the fact was true). A key success factor is the use of a central ontology that manages all stages of the production system, including data ingestion, data quality, security, optimization and presentation. The system has been in production use for over a year, proving the viability of the approach.
报告人简介：
Dean Allemang specializes in innovative applications of knowledge technology, is Founder and Principal Consultant at Working Ontologist LLC., a consultancy specializing in semantic web applications, currently with an industry focus in Finance. Formerly Chief Scientist at TopQuadrant, Inc., he has over 15 years experience in research, deployment and development of Semantic Technology systems. Dean Allemang has worked and studied extensively internationally as a Marshall Scholar at Trinity College, Cambridge. No stranger to innovation, he was twice winner of the Swiss Technology Prize. He has been keynote speaker for a number of Semantic Web conferences, including the Semantic Technologies and Business conference, the OWLED workshop, RuleML and at the 3rd Joint InternationalSemantic Technology Conference. As an internationally recognized expert in the Semantic web, he participated in the review board for the Digital Enterprise Research Institute (DERI) - the world’s largest Semantic Web research institute. He led TopQuadrant’s successful TopMIND training series, from which he drew much of the inspiration for his bestselling book (co-authored with Prof. Jim Hendler), Semantic Web for the Working Ontologist, now in second edition and translated into Chinese. Other book contributions include Abductive Inference (Josephson and Josephson, eds.) and Linking Enterprise Data (Wood, ed.). Dr. Allemang combines a strong formal background (MSc in Mathematics, University of Cambridge, PhD in Computer Science, Ohio State University) with years of experience applying knowledge-based technologies to real business problems.
报告题目：Big Data for Complex Business Analytics
报告人：Sheng-Chuan Wu
时间：2015年10月26日10：00-11：00
地点：数学馆201报告厅
报告摘要：
By combining the enormous modern and inexpensive computing power on Big Data platform such as Hadoop, data scientists can now perform sophisticated Data Mining operations on the zettabytes of digital data produced every minute. However, several challenges, namely heterogeneous data sources, convolute data relations and complex queries inherent to actionable business analytics, make it difficult to answer essential business questions from big data.For example, medical practitioners may want to explore the trove of patient medical records in a hospital to answer the following question, Find the probability of male, type-2 diabetes African American patients aged between 50 and 60, who were treated with Rosiglitazone and suffered from heart attack or stroke within 6 months. Such query involves many self joints plus temporal reasoning, not practical for a key-value database typical with Hadoop to perform. In this talk, Dr. Wu will describe a new analytic architecture, combining the popular big data Hadoop platform, semantic index and distributed query to extract actionable business insight from big data in nearly real-time. He will show the power of this new architecture with real-world examples in Healthcare and medical analytics.
报告人简介：
Dr. Sheng-Chuan Wu received his Ph.D. in Scientific Computing and Computer Graphics from Cornell University in the US. He has since graduation, involved in several software companies, including the founding of the first integrated CAD/CAM/CAE company. He has in the last 20 years worked as a senior corporate executive at the leading Artificial Intelligence and Semantic Technology company, Franz Inc in Silicon Valley, with responsibility in application development, marketing, consulting and new business development. Dr. Wu has also in many occasions collaborated with Bioinformatics experts from Harvard Medical School, Stanford University and Astra Zeneca, working with massive biological data. Dr. Wu has been focusing on Semantic Technology over the last 7 years. He has routinely lectured on AI and Semantic Technology at conferences. He has, since 2007, conducted more than 20 week-long workshops on Semantic Technology and Artificial Intelligence in Malaysia, China, India and other Asian countries. Additionally, Dr. Wu has consulted on several Big Data and Semantic Technology projects in the US and Asia.